于随机Markov链的风向时间序列模型研究

基于随机Markov链模型提出了一种风向时间序列模拟模型。该模型选取气象学中较为细致的风向划分方法-16风向,作为Markov链模型状态空间划分的依据。模型验证表明,随机Markov链模型生成的风向时间序列较好的保留了原始数据分布特性、自相关特性、功率谱特性等统计特性。这证明,通过Markov链建模,然后生成任意时间长度的风向时间序列的方法是可行的,且在加入阈值修正后,该模型的短期风向预测效果较好。

Study on the Time Series Model of Wind Direction Based on Stochastic Markov Chain

A time series model of wind direction is presented based on stochastic Markov chain model in this paper, in which the 16 wind-direction partitioning method in meteorology is used as the basis for partitioning the state spaces of stochastic Markov chain model. Model verification shows that the generation of time series of wind direction by stochastic Markov chain model can retains such better statistical characteristics as distribution characteristic of original data, autocorrelation coefficient and power spectrum characteristics. It is proved that it is feasible that the time series of wind direction in arbitrary time length is generated based on the stochastic Markov chain model, and the model has better prediction effect in forecasting wind direction by introducing threshold correction.